In one of our previous articles, we focused on why data cleaning is an integral part of working with data. This time, we will look at the practical side, i.e., what tools help ensure that data is high-quality, consistent, and ready for analysis. We will introduce both simple spreadsheet editors and more sophisticated cloud platforms.
Open science emphasizes the accessibility of research data, including in the humanities. But what does data mean in this context? And why is it not neutral? Read this article, which explores the philosophical and practical aspects of the concept of data in the humanities and highlights the importance of the humanities perspective for understanding data culture.
An often overlooked yet crucial stage of working with data is cleaning. It is at this stage that it is decided whether the results of the analysis will be truly reliable or whether they will result in an inaccurate picture of reality. Let's take a look at why data cleaning is indispensable for every step from modeling to interpretation, and why analysts should pay as much attention to it as they do to visualizations or conclusions themselves.
